8 accepted papers
Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization. However, existing methods struggle to reconcile fidelity and diversity, often hampered by distribution misalignment and overfitting, respectively. To mitigate this, we introduce Anomaly P…
Open-set supervised anomaly detection (OSAD) aims to identify unseen anomalies using limited anomalous supervision. However, existing prototype-based methods typically model normal data via a unimodal Gaussian prior, failing to capture inherent multi-modality and resulting in blurred decision bounda…
In Open-set Supervised Anomaly Detection (OSAD), the existing methods typically generate pseudo anomalies to compensate for the scarcity of observed anomaly samples, while overlooking critical priors of normal samples, leading to less effective discriminative boundaries. To address this issue,…
The rapid growth of multimedia-sharing platforms drives the development of recommender systems. While traditional ID-based methods for mining user behavior signals are well-studied, research into multimodal sequential recommendation remains nascent. Current approaches face three critical challenges:…
With the beneft of explicit object-oriented reasoning capabilities of scene graphs, scene graph-to-image generation has made remarkable advancements in comprehending object coherence and interactive relations. Recent state-of-the-arts typically predict the scene layouts as an intermediate represent…
3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vectset Diffusion Model (VDM). While recent advancements have shown promising results in generating high-resolution 3D shapes, VDM still struggles at high-speed generation.…
Model-free reinforcement learning (RL) combined with diffusion models has achieved significant progress in addressing complex continuous control tasks. However, a persistent challenge in RL remains the accurate estimation of Q-values, which critically governs the efficacy of policy optimization. Alt…
Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general…